• Energy Research

This work presents a case study of thermodynamic performance of a condenser used in a 210 MW thermal power station at Mejia in West Bengal, India. The analysis involves an improvement of actual overall heat transfer coefficient by varying tube materials and fouling resistance. Exergy Destruction Factor (EDF) is introduced to quantify the percentage of exergy loss from the condenser wall. From this study, it is revealed that the second law performance of the condenser increases with the increase in thermal conductivity of the tube materials, decreases with the increase in fouling resistance and decreases with the increase in condenser pressure at the turbine outlet. The actual overall heat transfer coefficient can be increased up to 6% by selecting better conducting tube materials. While the EDF decreases with the increase in cooling water temperature rise and increases with the increase in cooling water inlet temperature, EDF is found to decrease with the increased cooling water mass flow rate.

This article proposes a robust resilience enhancement method for a power distribution network in ice storms by the optimal routing of mobile de-icing devices (MDIDs) on congested transportation roads. We consider MDIDs as emergency vehicles, as other vehicles yield the right-of-way to MDIDs, and propose a de-icing schedule (DIS) to illustrate how MDIDs are routed on congested transportation roads. We further coordinate the DIS and MDID routing, which mitigate transportation routing congestions, with power distribution system operation, which considers distribution network reconfiguration and distributed energy resource dispatch. A two-stage robust model is proposed to manage the effects of ice storms forecast errors on both power distribution and urban transportation networks. The proposed model is reformulated as a mixed-integer second-order cone programming problem. The Benders decomposition and column-and-constraint generation algorithms are further utilized to solve the proposed MISOCP. Numerical results for the modified IEEE 33-bus 12-node, IEEE 123-bus 25-node, and 252-bus 80-node electricity-transportation systems show the effectiveness of the proposed model and solution technique for enhancing the power system resilience.

The growing interest in solar energy stems from its potential to reduce greenhouse gas emissions. Global horizontal irradiance (GHI) is a crucial determinant of the productivity of solar photovoltaic (PV) systems. Consequently, accurate GHI forecasting is essential for efficient planning, integration, and optimization of solar PV energy systems. This study evaluates the performance of six machine learning (ML) regression models—artificial neural network (ANN), decision tree (DT), elastic net (EN), linear regression (LR), Random Forest (RF), and support vector regression (SVR)—in predicting GHI for a site in northern Saudi Arabia known for its high solar energy potential. Using historical data from the NASA POWER database, covering the period from 1984 to 2022, we employed advanced feature selection techniques to enhance the predictive models. The models were evaluated based on metrics such as R-squared (R2), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE). The DT model demonstrated the highest performance, achieving an R2 of 1.0, MSE of 0.0, RMSE of 0.0, MAPE of 0.0%, and MAE of 0.0. Conversely, the EN model showed the lowest performance with an R2 of 0.8396, MSE of 0.4389, RMSE of 0.6549, MAPE of 9.66%, and MAE of 0.5534. While forward, backward, and exhaustive search feature selection methods generally yielded limited performance improvements for most models, the SVR model experienced significant enhancement. These findings offer valuable insights for selecting optimal forecasting strategies for solar energy projects, contributing to the advancement of renewable energy integration and supporting the global transition towards sustainable energy solutions.

The optimization of distributed generation technologies and storage systems are essential for a reliable, cost-effective, and secure system due to the uncertainties of Renewable Energy Sources (RESs) and load demand. In this study, two algorithms, the Multi-Objective Particle Swarm Optimization (MOPSO) and the Non-Dominant Sorting Genetic Algorithm II (NSGA-II) were utilized to design five different case studies (CSs) (photovoltaic (PV)/ wind turbine (WT)/ battery/ diesel generator (DG), PV/ WT/ battery/ fuel cell (FC)/ electrolyzer (EL)/ hydrogen tank (HT), PV/ WT/ battery/ grid-connected, PV/ WT/ battery/ grid-connected with Demand Response Program (DRP), and PV/ WT/ battery/ electric vehicle (EV)) to minimize life cycle cost (LCC), loss of power supply probability (LPSP), and CO\text2 emissions. In fact, different backups are provided for (PV/ WT/ battery), which is considered as the base system. Further, the uncertainties in RES and load were modeled by the Taguchi method, and Monte Carlo simulation (MCS) was used to model the uncertainties in EV to achieve accurate results. In addition, in CS4, a Demand Response Program (DRP) based on Time-of-Use (TOU) price is considered to study the effect on the number of specific components and other parameters. Finally, the simulation results verify that the NSGA-II calculation provides accurate and reliable outcomes compared to the MOPSO method, and the PV/WT/battery/ EV combination is the most suitable option among the five designed scenarios.

This paper develops a multi-level game-theoretic framework for determining a cost-effective defensive strategy for protecting power systems from false data injection attacks like load redistribution attacks. First, a multi-level optimization problem considering interactions among defenders, attackers and operators is modeled based on the minimax-regret decision rule, which is then reformulated as an equivalent bi-level mixed-integer linear programming problem. Next, an implicit enumeration algorithm is developed to find a globally optimal solution to this complex bi-level problem. Several acceleration techniques are introduced to improve the computation efficiency of the proposed method for large-scale power system applications. Last, the proposed defensive strategy is validated by case studies based on a six-bus test system and a modified two-area RTS-96 system.

Because of electricity markets, environmental concerns, transmission constraints, and variable renewable energy sources (VRES), coordinated operation of demand response (DR) and battery energy storage systems (BESS) has become critical. In turn, the optimal coordinated operation of DR and BESS by an entity can affect overall electricity market outcomes and transmission network conditions. The coordinated operation is desirable for the profit-seeking entity, but it may adversely affect the cost and revenues of other market participants or cause system congestion. Though few coordinated operation models already exist, our aim in this research is to provide a novel multi-objective optimization-based methodology for the coordinated operation of DR and BESS to boost market profit. Moreover, another goal is to simultaneously study the combined effects of such coordinated models on transmission networks and electricity markets for the first time. This paper has proposed a new method for coordinated DR and BESS utilization by a load-serving entity (LSE) to increase its profit. Moreover, it has employed agent-based modeling of the electricity systems (AMES) for testing our coordinated DR and BESS method under day-ahead market and transmission system conditions. Simulation results of case studies indicate that the operating costs of all LSEs decreased, and there was as much as 98,260 $/day in cost savings for BESS deploying LSE1. Although revenues of cheaper generation companies (GenCos) decreased, those of expensive GenCos increased or showed mixed trends. For example, GenCo 3 exhibits an 8765 $/day decrease in revenue for 25% BESS capacity, whereas a 6328 $/day increase in revenue for 37.5% BESS capacity. The variance of LMPs, widely used as a risk index, greatly decreased for the LSE utilizing the coordinated methodology, somewhat decreased for other LSEs but increased for cheaper GenCos with no LSE at the local node. Since BESS deployment decisions of an LSE can have system-wide or market-wide consequences, simulation analysis before deployment can help reduce market distortions or system congestions.

The widespread adoption of communication and control infrastructures will not only improve the microgrid system performance in normal conditions but also increase microgrid cybersecurity risks. Potential cyberattacks can deteriorate microgrid performances by corrupting and intercepting data exchanges among participating DERs, whereby microgrids deviate from desired operating conditions and stable microgrid operations are jeopardized. In this paper, a cross-layer control strategy is proposed to enhance the microgrid resilience against false data injection (FDI) and denial of service (DoS) attacks. On the one hand, the proposed control strategy will not interfere with microgrid normal operations when there are no cyberattacks. On the other hand, the proposed control strategy can effectively mitigate the impacts of FDI and DoS attacks on microgrids without relying on prompt detection and isolation of cyberattacks. The stability of the proposed control strategy is demonstrated using the Lyapunov theory under different scenarios, including without and with FDI and DoS attacks. The effectiveness of the proposed cross-layer resilient control strategy against cyberattacks is validated in a 12-bus microgrid system using time-domain PSCAD/EMTDC simulations.

Saudi Arabia (SA) currently relies on fossil fuels to address its escalating electricity demand and rapid industrialization, a practice that significantly contributes to climate change. This study underscores the potential of solar energy as a key renewable energy source (RES) for SA, with a specific focus on Concentrated Solar Power (CSP). CSP stands out due to its capacity to provide dispatchable electricity coupled with thermal energy storage (TES). This research introduces an integrated energy model encompassing both site suitability and techno-economic analyses tailored for utility-scale CSP technology. The investigation unfolds in two phases: site suitability analysis and techno-economic assessment, each designed to scrutinize the viability and applicability of CSP technology for power generation in SA’s western region. In the initial phase, an innovative approach, leveraging Fuzzy-Boolean Logic and Analytical Hierarchy Process (AHP) through GIS tools, is employed to identify optimal CSP plant locations. This method offers a more comprehensive and robust analysis by accounting for uncertainty and ambiguity in decision-making. Criteria are prioritized based on relative importance, contributing a novel dimension to the field. The analysis reveals that 70% of the province’s land is suitable for CSP deployment, with Makkah, Taif, Al-Khumra, and Turbah identified as the most favorable locations. In the second phase, two established CSP plants, Shams-1 and Noor III, are utilized to evaluate the technical and economic feasibility of CSP in five selected sites within the most suitable areas. The analysis unveils the lowest levelized cost of electricity (LCOE) for utility-scale CSP plants in Makkah province, standing at 9.58 ¢/kWh for parabolic trough (PT) technology and 9.17 ¢/kWh for solar power tower (SPT) technology. Sensitivity analysis of TES indicates that CSP plants with 8 hours of storage exhibit the optimal configuration, producing electricity with the lowest LCOE and the highest capacity factor (CF). This comprehensive study establishes CSP as a viable and promising renewable energy (RE) technology for SA. The proposed site selection methodology facilitates the identification of suitable locations for CSP plants, while the techno-economic analysis demonstrates that CSP plants equipped with TES are both cost-effective and reliable.

Considering the high penetration of distributed energy resources (DERs), extensive communication networks are deemed necessary to realize the coordinated control of DERs in microgrids (MGs). The reliance on communication networks introduces potential cyber security issues into MGs. However, such attacks are difficult to be detected when a global situational awareness is lacking in distributed systems. In this paper, we analyze the impacts of cyber-attacks on communication links, local controllers, and master controller, which would drive MGs away from optimal operating points. We propose a cyber-attack resilient distributed control strategy in which each participant can detect and isolate corrupted links and controllers in a timely and economic fashion. The proposed control strategy is effective against time-varying attack signals and successive attacks while retaining the MG operational merits. The proposed control strategy is examined for different types of attacks. In this case, we consider various operating conditions and apply the time-domain PSCAD/EMTDC to the simulation and analyses of two MG systems.